Show Notes
In this episode, Mat and Phil discuss a book chapter by Glenn Stockwell, in which he writes about professional development and learner training for AI, highlighting the importance of ongoing reflection, learning training, and documenting professional growth. Glenn is the editor in chief of the journal Computer Assisted Language Learning and a professor of Applied Linguistics at the Education University of Hong Kong. In this episode: Glenn Stockwell: https://www.eduhk.hk/en/experts/professor-stockwell-glenn Glenn Stockwell's chapter: Professional development and learner training. https://castledown.online/reference/9781763711600-12/ The book with Glenn’s chapter: Yijen Wang, Antonie Alm, and Gilbert Dizon (Eds.), Insights into AI and language teaching and learning. Castledown Publishers. https://castledown.online/reference/9781763711600/ Mat’s chapter in the same book: ICALL and AI: Seven lessons from seventy years. https://doi.org/10.29140/9781763711600-02
More from Opening AI for Language Learning
When should you use AI, and when should you leave it alone? In today’s episode, we dig into the central question of Sam Illingworth’s book Slow AI: Know When to Use AI and When to Leave it Alone, exploring what it means to move beyond AI tools and techniques and toward using AI in ways that are genuinely worthwhile. Considering a variety of language teaching practices and processes—from adapting texts to different reading levels to creating lesson plans or grading student work—Mat and Phil discuss the importance of judging when a task can truly benefit from AI and when AI removes something valuable, whether it’s human judgment or processes that are themselves essential to learning. In this episode: Sam Illingworth’s Slow AI: Knowing When to Use AI and When to Leave It Alone https://samillingworth.com/book
Mat and Phil look back to look forward: What can the history of language teaching teach us about its future with AI? If generative AI represents something fundamentally new, does responding to it require leaving behind what language educators already know? Drawing on Mat’s forthcoming chapter on language teacher education before generative AI, our hosts reflect on what established knowledge about language, learning, and pedagogy can contribute to the decisions educators are making about AI today. How does AI challenge existing practices in teaching and assessment? What does it mean for the importance of language awareness? And, perhaps most importantly, how can educators decide when to rely on technology and when to rely on human expertise? Making the case for treating generative AI as genuinely new without leaving behind what we already know, the conversation highlights the linguistic, pedagogical, and human foundations that can help (teacher) educators navigate what comes next. In this episode: Mat is blogging about his chapter for the Cambridge Handbook of Artificial Intelligence and Language Teacher Education. Go to https://PantaRhei.press and search for the posts with “Beyond AI” in the title. Hubbard, Philip and Mathias Schulze (2025) AI and the future of language teaching – Motivating sustained integrated professional development (SIPD). International Journal of Computer Assisted Language Learning and Teaching 15.1., 1–17. DOI:10.4018/IJCALLT.378304 https://www.igi-global.com/gateway/article/full-text-html/378304
How can language teachers keep up with AI when the technology itself keeps changing? And how can teacher educators prepare future teachers for a landscape that may look very different just a few years from now? Drawing on Phil’s forthcoming chapter on Sustained Integrated Professional Development, Mat and Phil explore whether learning about AI needs to become an ongoing part of teacher educators’ professional practice. Do teacher educators need to be AI experts before they can prepare future teachers? What can educators learn by experimenting with AI themselves? In this episode, our hosts make the case for approaching AI not as something educators can learn once and master, but as an evolving area of professional learning—one that teachers and teacher educators can navigate together, from experiencing AI as a language learner and collaborating with colleagues to setting manageable goals, experimenting with new tools, and reflecting on successes and failures. In this episode: Contact Phil by email, if you would like to read a draft version of this chapter for the Cambridge Handbook of Artificial Intelligence and Language Teacher Education. ([email protected]) Hubbard, Philip and Mathias Schulze (2025) AI and the future of language teaching – Motivating sustained integrated professional development (SIPD). International Journal of Computer Assisted Language Learning and Teaching 15.1., 1–17. DOI:10.4018/IJCALLT.378304 https://www.igi-global.com/gateway/article/full-text-html/378304
What does it mean to understand a language? Today’s generative AI tools can produce remarkably convincing linguistic forms, but can these systems understand the language they produce? In today’s episode, Mat and Phil explore John Searle’s Chinese Room thought experiment and its argument that a system can produce appropriate linguistic forms without understanding their meaning. What can language learners gain from interacting with a machine that simulates language but does not understand it? Can learners engage in languaging—constructing meaning, knowledge, and experience through language and participating in human communities—by simply interacting with a chatbot? Our hosts explore these questions while also considering the opportunities AI chatbots offer language learners, from conversation practice and individualized language exposure to incidental language learning. Along the way, they reflect on what these possibilities—and their limitations—can teach language educators about the role of generative AI in language learning and the continued importance of teachers and human interaction.
What can generative AI do for language teaching and learning—and where does it fall short? Mat and Phil beam us back to Oxford, Ohio, for Part 2 of their conversations from the 2026 CALICO Conference, exploring AI for language learning, language teacher education, AI-generated feedback, and conversational AI through four perspectives. Dorothy Chun, Professor Emerita of Education and Applied Linguistics at UC Santa Barbara and recipient of the 2026 CALICO Lifetime Achievement Award, reflects on what decades of experience with language-learning technology can teach us about the current AI moment, including how to move beyond the hype to identify what AI can and cannot do and whether AI can reproduce the social, pragmatic, and intercultural dimensions of human interaction. Bin Zou, Professor of Applied Linguistics at Xi'an Jiaotong-Liverpool University in China, examines the opportunities and challenges of generative AI for language teachers, including AI feedback and ways educators can incorporate their own pedagogical expertise into general-purpose AI tools. Visiting Professor of Applied Linguistics at Ohio University Francesca Marino considers AI and language teacher education, including how U.S. universities are preparing future language teachers and CALL researchers to integrate emerging technologies. Finally, Ben Altschuler, CEO of Speakology AI, explains how conversational AI can support language learning through realistic AI avatars and teacher-designed speaking activities. In this episode: CALICO 2027 in Fairbanks, Alaska: https://calico.org/calico-2027/ Dorothy Chun: https://education.ucsb.edu/people/emeriti-faculty/dorothy-chun Bin Zou: https://scholar.xjtlu.edu.cn/en/persons/BinZou/ Francesca Marino: https://www.ohio.edu/directory/francesca-marino Ben Altschuler, CEO of Speakology AI: https://speakology.ai/